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Luo W, Wu J, Chen Z, Guo P, Zhang Q, Lei B, Chen Z, Li S, Li C, Liu H, Ma T, Liu J, Chen X, Ding Y. Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal. Endocrine 2024:10.1007/s12020-024-03931-z. [PMID: 38982023 DOI: 10.1007/s12020-024-03931-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/13/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed promise in assessing bone structure characteristics and determining the risk of fragility fracture. AIMS To evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragility fractures retrospectively in postmenopausal women, and compared it with the traditional parameter of QUS, speed of sound (SOS), and bone mineral density (BMD) acquired with dual X-ray absorptiometry (DXA). METHODS Using QUS, RF signal and SOS were acquired for 246 postmenopausal women. An MResNet was utilized, based on the RF signal, to categorize individuals with an elevated risk of fragility fracture. DXA was employed to obtain BMD at the lumbar, hip, and femoral neck. The fracture history of all adult subjects was gathered. Analyzing the odds ratios (OR) and the area under the receiver operator characteristic curves (AUC) was done to evaluate the effectiveness of various methods in discriminating fragility fracture. RESULTS Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was competent to discriminate any fragility fracture (OR = 2.64; AUC = 0.74), Vertebral fracture (OR = 3.02; AUC = 0.77), and non-vertebral fracture (OR = 2.01; AUC = 0.69). After being modified by clinical covariates, the efficiency of MResNet was further improved to OR = 3.31-4.08, AUC = 0.81-0.83 among all fracture groups, which significantly surpassed QUS-SOS (OR = 1.32-1.36; AUC = 0.60) and DXA-BMD (OR = 1.23-2.94; AUC = 0.63-0.76). CONCLUSIONS This pilot cross-sectional study demonstrates that the MResNet model based on the ultrasonic RF signal shows promising performance in discriminating fragility fractures in postmenopausal women. When incorporating clinical covariates, the efficiency of the modified MResNet is further enhanced, surpassing the performance of QUS-SOS and DXA-BMD in terms of OR and AUC. These findings highlight the potential of the MResNet as a promising approach for fracture risk assessment. Future research should focus on larger and more diverse populations to validate these results and explore its clinical applications.
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Affiliation(s)
- Wenqiang Luo
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China
- Bioland Laboratory, Guangzhou, 510320, P.R. China
| | - Jionglin Wu
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China
- Bioland Laboratory, Guangzhou, 510320, P.R. China
| | - Zhiwei Chen
- School of Biomedical Engineering, Health Science Center, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Peidong Guo
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China
- Bioland Laboratory, Guangzhou, 510320, P.R. China
| | - Qi Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, National Innovation Center for Advanced Medical Devices, Shenzhen, 518126, China
| | - Baiying Lei
- School of Biomedical Engineering, Health Science Center, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Zhong Chen
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China
- Bioland Laboratory, Guangzhou, 510320, P.R. China
| | - Shixun Li
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China
- Bioland Laboratory, Guangzhou, 510320, P.R. China
| | - Changchuan Li
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China
- Bioland Laboratory, Guangzhou, 510320, P.R. China
| | - Haoxian Liu
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China
- Bioland Laboratory, Guangzhou, 510320, P.R. China
| | - Teng Ma
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, National Innovation Center for Advanced Medical Devices, Shenzhen, 518126, China.
| | - Jiang Liu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, P.R. China.
| | - Xiaoyi Chen
- Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315020, P.R. China.
| | - Yue Ding
- Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P.R. China.
- Bioland Laboratory, Guangzhou, 510320, P.R. China.
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Rodriguez Betancourt A, Samal A, Chan HL, Kripfgans OD. Overview of Ultrasound in Dentistry for Advancing Research Methodology and Patient Care Quality with Emphasis on Periodontal/Peri-implant Applications. Z Med Phys 2023; 33:336-386. [PMID: 36922293 PMCID: PMC10517409 DOI: 10.1016/j.zemedi.2023.01.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/20/2022] [Accepted: 01/11/2023] [Indexed: 03/14/2023]
Abstract
BACKGROUND Ultrasound is a non-invasive, cross-sectional imaging technique emerging in dentistry. It is an adjunct tool for diagnosing pathologies in the oral cavity that overcomes some limitations of current methodologies, including direct clinical examination, 2D radiographs, and cone beam computerized tomography. Increasing demand for soft tissue imaging has led to continuous improvements on transducer miniaturization and spatial resolution. The aims of this study are (1) to create a comprehensive overview of the current literature of ultrasonic imaging relating to dentistry, and (2) to provide a view onto investigations with immediate, intermediate, and long-term impact in periodontology and implantology. METHODS A rapid literature review was performed using two broad searches conducted in the PubMed database, yielding 576 and 757 citations, respectively. A rating was established within a citation software (EndNote) using a 5-star classification. The broad search with 757 citations allowed for high sensitivity whereas the subsequent rating added specificity. RESULTS A critical review of the clinical applications of ultrasound in dentistry was provided with a focus on applications in periodontology and implantology. The role of ultrasound as a developing dental diagnostic tool was reviewed. Specific uses such as soft and hard tissue imaging, longitudinal monitoring, as well as anatomic and physiological evaluation were discussed. CONCLUSIONS Future efforts should be directed towards the transition of ultrasonography from a research tool to a clinical tool. Moreover, a dedicated effort is needed to introduce ultrasonic imaging to dental education and the dental community to ultimately improve the quality of patient care.
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Affiliation(s)
| | - Ankita Samal
- Department of Radiology, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Hsun-Liang Chan
- Department of Periodontology and Oral Medicine, Dental School, University of Michigan, Ann Arbor, MI, USA
| | - Oliver D Kripfgans
- Department of Radiology, Medical School, University of Michigan, Ann Arbor, MI, USA
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